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采用室内热舒适性控制的变风量空调系统节能控制研究 总被引:4,自引:0,他引:4
在对变风量空调系统及控制系统分析的基础上,利用DDC控制器可采集多点和多种信号的优点,提出采用室内热舒适性控制取代室内温度控制的控制方案。仿真试验结果证明,同常规的室内温度控制方案相比,室内热舒适性控制方案可以较好地改善室内的热舒适性,同时,在保证室内热舒适性前提下,采用室内热舒适性控制方案不仅能够保证控制的稳定性,而且有较好的节能作用。 相似文献
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《节能》2016,(10)
为探究高校学生公寓春季室内热舒适状况,采用现场测试与问卷调查相结合的方法对兰州市某高校14间学生公寓室内热环境状况进行了现场调查研究,共获得181份有效人体热反应样本。运用统计分析法对受试者的热感觉、衣服热阻与操作温度进行了回归分析。结果表明,春季公寓内学生着装的平均服装热阻为0.689clo,90.1%的学生对室内20.4℃的平均温度表示接受;实测热中性温度为17.8℃,预测热中性温度为19.8℃,所期望的室内温度为18.7℃;80%的学生可接受的操作温度范围是17.7~22.1℃,其热接受温度下限比同属寒冷地区西安市的高3.2℃。该研究结果可为兰州高校学生公寓室内热环境的控制和制定其室内热舒适标准提供参考。 相似文献
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A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables — supply air temperature and supply air duct static pressure set points — are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system. 相似文献
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A data-driven approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system in an office building is presented. A neural network (NN) algorithm is used to build a predictive model since it outperformed five other algorithms investigated in this paper. The NN-derived predictive model is then optimized with a strength multi-objective particle-swarm optimization (S-MOPSO) algorithm. The relationship between energy consumption and thermal comfort measured with temperature and humidity is discussed. The control settings derived from optimization of the model minimize energy consumption while maintaining thermal comfort at an acceptable level. The solutions derived by the S-MOPSO algorithm point to a large number of control alternatives for an HVAC system, representing a range of trade-offs between thermal comfort and energy consumption. 相似文献
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《Energy Conversion and Management》2005,46(9-10):1579-1593
Saving consumable energy and maintaining the thermal comfort level are two main topics in the heating, ventilating and air conditioning (HVAC) control field. The reliability of the controller is important as well. This paper proposes a least enthalpy estimator (LEE) that combines the definition of thermal comfort level and the theory of enthalpy into a load predicting way to provide timely suitable settings for a fan coil unit (FCU) fuzzy controller used in HVAC. According to the settings, including temperature and relative humidity, the fuzzy controller can make decisions and adjust the output of the FCU system. From actual experiments, the LEE-based FCU fuzzy controller can achieve the requirements of the FCU control system such as thermal comfort, energy efficiency and reliability. 相似文献
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For an installed centralized heating, ventilating and air conditioning (HVAC) system, appropriate energy management measures would achieve energy conservation targets through the optimal control and operation. The performance optimization of conventional HVAC systems may be handled by operation experience, but it may not cover different optimization scenarios and parameters in response to a variety of load and weather conditions. In this regard, it is common to apply the suitable simulation–optimization technique to model the system then determine the required operation parameters. The particular plant simulation models can be built up by either using the available simulation programs or a system of mathematical expressions. To handle the simulation models, iterations would be involved in the numerical solution methods. Since the gradient information is not easily available due to the complex nature of equations, the traditional gradient-based optimization methods are not applicable for this kind of system models. For the heuristic optimization methods, the continual search is commonly necessary, and the system function call is required for each search. The frequency of simulation function calls would then be a time-determining step, and an efficient optimization method is crucial, in order to find the solution through a number of function calls in a reasonable computational period. In this paper, the robust evolutionary algorithm (REA) is presented to tackle this nature of the HVAC simulation models. REA is based on one of the paradigms of evolutionary algorithm, evolution strategy, which is a stochastic population-based searching technique emphasized on mutation. The REA, which incorporates the Cauchy deterministic mutation, tournament selection and arithmetic recombination, would provide a synergetic effect for optimal search. The REA is effective to cope with the complex simulation models, as well as those represented by explicit mathematical expressions of HVAC engineering optimization problems. 相似文献
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暖通空调系统的高效节能运行高度依赖于传感器测量的准确性。在传感器全寿命运行周期中,不可避免发生各种故障,影响其准确性。为探究传感器故障对不同暖通空调系统的影响,文章以室温传感器偏差故障为例,针对武汉地区某办公建筑,同时开展地源热泵和"冷水机组+锅炉"两种暖通空调系统形式的能耗建模,对比分析-5℃~+5℃偏差故障对两种系统运行能耗、工作性能及室内热舒适性的影响差异。结果表明:室温传感器故障的偏差幅值方向对两种系统运行能耗、工作性能及室内热舒适性的影响规律不同。其中,地源热泵系统能耗受室温传感器偏差故障影响相对更小。 相似文献
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This research accounts for the outcome of a major cloud-based smart dual fuel switching system (SDFSS) project, which is a dual-fuel integrated hybrid heating, ventilation, and air conditioning (HVAC) system in residential homes. The SDFSS was developed to enable optimized, flexible, and cost-effective switching between the natural gas furnace and electric air source heat pump (ASHP). In order to meet the optimal energy consumption requirements in the house and provide thermal comfort for the residents, various high-quality sensors and meters were installed to record multiple data points inside and outside the house. The performance of the system was monitored in the long term, which is a common practice in energy monitoring projects. Outdoor temperature data plays the most crucial role in operating HVAC systems and also is a key variable in the decision-making algorithm of the SDFSS controller. Therefore, this study introduces an innovative and unique approach to obtain the outdoor temperature that could potentially replace high precision sensors with a data-driven model utilizing weather station data at a time resolution of 2 minutes and 1 hour. In this work, a series of artificial neural network algorithms were developed, optimized, and implemented to predict the outdoor temperature with an average of 0.99 coefficient of correlation (R), 1.011 mean absolute error (MAE), and 1.315 root mean square error (RMSE). It has been demonstrated that the developed ANN is a reliable and powerful tool in predicting outdoor temperature. Thus, the proposed model is strongly suggested to be implemented as an alternative to temperature sensors in hybrid energy systems or similar systems requiring accurate ambient temperature measurements. 相似文献
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Thermal management of a solid oxide fuel cell (SOFC) stack essentially involves control of the temperature within a specific range in order to maintain good performance of the stack. In this paper, a nonlinear temperature predictive control algorithm based on an improved Takagi-Sugeon (T-S) fuzzy model is presented. The improved T-S fuzzy model can be identified by the training data and becomes a predictive model. The branch-and-bound method and the greedy algorithm are employed to set a discrete optimization and an initial upper boundary, respectively. Simulation results show the advantages of the model predictive control (MPC) based on the identified and improved T-S fuzzy model for an SOFC stack. 相似文献
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Cai-Hua Liang Xiao-Song Zhang Xiu-Wei Li Zhen-Qian Chen 《Applied Thermal Engineering》2010,30(8-9):892-899
A new defrosting method – the sensible heat defrosting method, aiming at shelving the various disadvantages of the conventional reverse cycle defrosting was proposed in this paper. The mechanism and process of this method was analysed. To guarantee the reliability, the self-organizing control algorithm with self-learning function was introduced based on the cardinal fuzzy control algorithm. Moreover, the control strategy was enacted; the corresponding self-organizing fuzzy control system was developed; the Micro Controller Unit (MCU) based control unit was accomplished; and the experimental study was conducted to investigate the sample machine of air-source heat pump system. The results of the experiments showed that the self-organizing control algorithm has good control characteristic and effect. On one hand, the adverse shock from the conventional reverse cycle defrosting to the refrigeration system could be avoided through this proposed method; on the other hand, the “oil rush” could also be eliminated. Besides, the thermal comfort could be greatly improved since the temperature fluctuation range of the supplied water is narrowed by applying this new method in practice. 相似文献